flennerhag/mlens
ML-Ensemble – high performance ensemble learning
This tool helps data scientists and machine learning engineers combine multiple predictive models into a single, more powerful 'ensemble' model efficiently. You provide your individual models and data, and it outputs a high-performance ensemble that often achieves better predictions. It's designed for practitioners who want to build complex, optimized predictive systems with ease.
863 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to build advanced, highly performant ensemble models for predictive tasks, and want to manage computational resources effectively.
Not ideal if you are new to machine learning and just need a simple, single predictive model without the overhead of ensemble techniques.
Stars
863
Forks
111
Language
Python
License
MIT
Category
Last pushed
Nov 13, 2023
Commits (30d)
0
Dependencies
2
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/flennerhag/mlens"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
iamDecode/sklearn-pmml-model
A library to parse and convert PMML models into Scikit-learn estimators.
vecxoz/vecstack
Python package for stacking (machine learning technique)
yzhao062/combo
(AAAI' 20) A Python Toolbox for Machine Learning Model Combination
aws-samples/aws-machine-learning-university-dte
Machine Learning University: Decision Trees and Ensemble Methods
jeffrichardchemistry/pyECLAT
A package for association analysis using the ECLAT method.